




import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase								=	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
     import run_clm_flax
     import run_flax_glue
     import run_flax_ner
     import run_mlm_flax
     import run_qa
     import run_summarization_flax
     import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase								=	logging.getLogger()



def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					argparse.ArgumentParser()
     parser.add_argument("-f"     )
     lowerCAmelCase__            =					parser.parse_args()
     return args.f



def 				_A  (		lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   Any="eval"     ):
     """simple docstring"""

     lowerCAmelCase__            =					os.path.join(lowerCAmelCase_  ,							F'{split}_results.json'     )
     if os.path.exists(lowerCAmelCase_     ):
          with open(lowerCAmelCase_  ,							"r"     ) as f:
               return json.load(lowerCAmelCase_     )
     raise ValueError(F'can\'t find {path}'     )


UpperCamelCase								=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    def   a (			self	:	str  ) ->			Tuple:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

         with patch.object(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_flax_glue.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_accuracy"]  ,      0.75  )


    @slow
    def   a (			self	:	int  ) ->			str:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

         with patch.object(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_clm_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertLess(result["eval_perplexity"]  ,      100  )


    @slow
    def   a (			self	:	Any  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

         with patch.object(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_summarization_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  ,      split="test"  )
              self.assertGreaterEqual(result["test_rouge1"]  ,      10  )
              self.assertGreaterEqual(result["test_rouge2"]  ,      2  )
              self.assertGreaterEqual(result["test_rougeL"]  ,      7  )
              self.assertGreaterEqual(result["test_rougeLsum"]  ,      7  )


    @slow
    def   a (			self	:	int  ) ->			str:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

         with patch.object(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_mlm_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertLess(result["eval_perplexity"]  ,      42  )


    @slow
    def   a (			self	:	Optional[Any]  ) ->			Optional[int]:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

         with patch.object(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_ta_mlm_flax.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_accuracy"]  ,      0.42  )


    @slow
    def   a (			self	:	Tuple  ) ->			Optional[Any]:
         # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
         lowerCAmelCase__            =					7 if get_gpu_count() > 1 else 2

         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

         with patch.object(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_flax_ner.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_accuracy"]  ,      0.75  )
              self.assertGreaterEqual(result["eval_f1"]  ,      0.3  )


    @slow
    def   a (			self	:	Optional[Any]  ) ->			Tuple:
         lowerCAmelCase__            =					self.get_auto_remove_tmp_dir()
         lowerCAmelCase__            =					f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

         with patch.object(SCREAMING_SNAKE_CASE__  ,      "argv"  ,      SCREAMING_SNAKE_CASE__  ):
              run_qa.main()
              lowerCAmelCase__            =					get_results(SCREAMING_SNAKE_CASE__  )
              self.assertGreaterEqual(result["eval_f1"]  ,      30  )
              self.assertGreaterEqual(result["eval_exact"]  ,      30  )






"""simple docstring"""




from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase				:     Union[str, Any]					=				logging.get_logger(__name__)

lowerCAmelCase				:     Union[str, Any]					=				{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"mctct"





				def __init__(			self						,					_a=8_065						,					_a=1_536						,					_a=36						,					_a=6_144						,					_a=4						,					_a=384						,					_a=920						,					_a=1e-5						,					_a=0.3						,					_a="relu"						,					_a=0.02						,					_a=0.3						,					_a=0.3						,					_a=1						,					_a=0						,					_a=2						,					_a=1						,					_a=0.3						,					_a=1						,					_a=(7,)						,					_a=(3,)						,					_a=80						,					_a=1						,					_a=None						,					_a="sum"						,					_a=False						,					**_a						,					):





							"""simple docstring"""
							super().__init__(**_a						,					pad_token_id=_a						,					bos_token_id=_a						,					eos_token_id=_a					)
							lowerCamelCase      =   vocab_size
							lowerCamelCase      =   hidden_size
							lowerCamelCase      =   num_hidden_layers
							lowerCamelCase      =   intermediate_size
							lowerCamelCase      =   num_attention_heads
							lowerCamelCase      =   attention_head_dim
							lowerCamelCase      =   max_position_embeddings
							lowerCamelCase      =   layer_norm_eps
							lowerCamelCase      =   layerdrop
							lowerCamelCase      =   hidden_act
							lowerCamelCase      =   initializer_range
							lowerCamelCase      =   hidden_dropout_prob
							lowerCamelCase      =   attention_probs_dropout_prob
							lowerCamelCase      =   pad_token_id
							lowerCamelCase      =   bos_token_id
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   conv_glu_dim
							lowerCamelCase      =   conv_dropout
							lowerCamelCase      =   num_conv_layers
							lowerCamelCase      =   input_feat_per_channel
							lowerCamelCase      =   input_channels
							lowerCamelCase      =   conv_channels
							lowerCamelCase      =   ctc_loss_reduction
							lowerCamelCase      =   ctc_zero_infinity

							# prevents config testing fail with exporting to json
							lowerCamelCase      =   list(_a					)
							lowerCamelCase      =   list(_a					)

							if len(self.conv_kernel					) != self.num_conv_layers:
										raise ValueError(
										    """Configuration for convolutional module is incorrect. """
										    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
										    f'but is `len(config.conv_kernel) = {len(self.conv_kernel					)}`, '
										    f'`config.num_conv_layers = {self.num_conv_layers}`.'					)



